Double-cohesion learning based multiview and discriminant palmprint recognition

TL;DR: Zhang et al. as discussed by the authors proposed a novel generalized distance constraint dubbed intra-class and inter-class kernel constraint (IIKC), which explicitly models the category kernel distance and focuses on helping the original LDA capture more discriminant features to further improve the separability and magnitude difference between nearby data points.

TL;DR: A novel Federated Metric Learning (FedML) method is proposed to address the issue of data privacy and data islands in palmprint recognition and demonstrates that FedML can outperform other methods by a large margin and obtain promising performance.

TL;DR: Wang et al. as discussed by the authors proposed a new feature called the middle of the triangle's side (MTS) derived from each minutiae pair of a triangle-based representation to mitigate the negative effect of the geometric distortion and creases.

TL;DR: This paper provides a comprehensive survey of palmprint recognition methodologies, analyzing 20 datasets and classifying them into contact-based and contactless-based categories, while proposing a novel taxonomy for feature extraction approaches and highlighting open challenges and emerging trends.

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.

TL;DR: The system consists of a novel device for online palmprint image acquisition and an efficient algorithm for fast palmprint recognition, and a robust image coordinate system is defined to facilitate image alignment for feature extraction.

TL;DR: Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)].